Hub Station Stall and Occupancy Behavior Image Dataset
#object detection #behavior recognition #urban management #traffic monitoring #public safety
- 5000 records
- 1.5G
- JPG/PNG/JSON
- CATL
MOBIUSI INC
Updated:2026-04-16
AI Analysis & Value Prop
The current transportation industry faces increasing difficulty in urban management, especially during peak periods where stall and occupancy behaviors frequently disrupt traffic flow and city appearance. Existing monitoring systems often cannot recognize these behaviors in real time and accurately, leading to delays in management actions. This dataset aims to provide high-quality image data to help researchers and developers train more accurate object detection models for real-time identification and handling of such behaviors. The dataset includes 5000 high-resolution images collected from major roads and high-traffic areas in multiple cities, captured using professional cameras under various weather and lighting conditions. To ensure data quality, we employed multiple rounds of annotation, consistency checks, and expert reviews as quality control measures. Data is stored in JPG format and organized using a hierarchical folder structure.
Dataset Insights
Sample Examples
9e58f037**.jpg|1706*1279|167.38 KB
203d9c7e**.jpg|1706*1279|152.58 KB
32210916**.jpg|1706*1279|153.68 KB
952850b6**.jpg|1706*1279|154.19 KB
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| object_type | string | This field identifies the type of objects detected in the image, such as stalls, vehicles, pedestrians, etc. |
| location_coordinates | string | This field describes the location coordinates (e.g., x, y) of the detected objects in the image. |
| activity_type | string | This field indicates the type of activity shown in the image, such as street vending, road occupation, etc. |
| weather_condition | string | This field describes the weather conditions at the time the image was captured, such as sunny, rainy, etc. |
| time_of_day | string | This field indicates the time period when the image was captured, such as morning, afternoon, evening, etc. |
| number_of_people | integer | This field indicates the number of people detected in the image. |
| vehicle_count | integer | This field indicates the number of vehicles detected in the image. |
| stall_count | integer | This field indicates the number of stalls detected in the image. |
Compliance Statement
| Authorization Type | Proprietary - Commercial AI Training License (No Redistribution) |
| Commercial Use | Requires exclusive subscription or authorization contract (monthly or per-invocation charging) |
| Privacy and Anonymization | No PII, no real company names, simulated scenarios follow industry standards |
| Compliance System | Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs |
Frequently Asked Questions
- What is the Hub Station Stall and Obstruction Behavior Image Dataset?
- The Hub Station Stall and Obstruction Behavior Image Dataset is focused on identifying informal stalls and obstructions at transport hub stations specifically for object detection tasks in urban management.
- What traffic-related problems can this dataset help solve?
- This dataset can help solve issues relating to informal stall management and obstruction monitoring in transport hub areas, enhancing urban traffic management efficiency.
- Why is the Hub Station Stall and Obstruction Behavior Image Dataset suitable for object detection tasks?
- This dataset is suitable for object detection tasks as it includes a large number of annotated images of stalls and obstructions, aiding in training models to better recognize and detect such behaviors.
- How does this dataset support traffic decision-making in urban management?
- By providing detection information on stalls and obstructions at transport hubs, the dataset aids urban managers in making more precise traffic decisions and optimizing the use of public space.
- What adverse effects do stall and obstruction behaviors have on traffic?
- These behaviors can cause traffic jams, hinder emergency vehicle access, reduce traffic efficiency, and pose potential safety hazards.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2025,
title={Hub Station Stall and Occupancy Behavior Image Dataset},
author={MOBIUSI INC},
year={2025},
url={https://www.mobiusi.com/datasets/96ff0227e68905c755292b8f4a958f44?cate=2},
urldate={2025-09-15},
keywords={transportation dataset, object detection, image dataset, occupancy behavior recognition},
version={1.0}
}Using this in research? Please cite us.
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